According to Wu et al.  [1]}, we evaluate the quality of features with a simple weighted \(k\)  Nearest Neighbor classifier. We freeze the parameters of pre-trained model and extract the features of class embedding for the train and validation dataset. As shown in Table REF , we evaluate different values for \(k\)  and find that the setting of 10 is consistently leading to the best accuracy across our runs. More importantly, we evaluate Top-1 accuracy in the validation dataset.
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